Spatio-temporal redistribution of mTHPC from lipid nanovesicles in chick chorioallantoic membrane model
Bibliographic record
Abstract
Liposomal formulations of mTHPC (Foslip, Fospeg) were initially proposed to improve its chemico-biological properties and distribution in targeted tissues. They permitted the preservation of the monomeric state of the molecule and the enhancement of its pharmacokinetic and clearance. Previous experiments realized by our laboratory with mTHPC (Mitra et al. 2005, Garrier et al. 2010; Lassalle et al 2009) demonstrated the primary importance of spatio-temporal distribution of the molecule for optimization of PDT dosimetric parameters (drug- light interval, drug dose...). Optimization of PDT parameters for photosensitizers embedded into the liposomes also requires the comprehension of the PSs repartition from lipid nanovesicles. However, there are not data concerning the distribution of mTHPC in relation with the degradation of lipid nanovesicules administered in vivo. This analysis is actually conducted by using the chick chorioallantoic membrane (CAM) model with lipid nanovesicles which contain mTHPC and a fluorescent probe of nanovesicle integrity pyrene. Activity of pyrene is based on a mechanism of FRET (Fluorescence Resonance Energy Transfer) where pyrene plays the role of energy donor while mTHPC stands for an acceptor of energy. The measurement of their fluorescence lifetimes by FLIM technique (Fluorescence Lifetime Imaging Microscopy) reflects the state of integrity of the lipid nanovesicule. The energy transfer in intact liposome was confirmed by a strong diminution (>1000 fold) of fluorescence lifetime of pyrene in the presence of mTHPC. The optimization of this technique permits to study fluorescence lifetime in CAM in relation with the time after intravenous injection of mTHPC loaded in liposome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".